In this paper, we report the first example of impact sensitivity prediction based on the genetic function approximation (GFA) as a regression method. The prediction is applicable for a wide variety of chemical families, which include nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc. Within this work, we have obtained 7 empirical models (with 27-32 basis functions), which all provide 0.80≤R≤0.83 and 7.2 J≤RMSE≤7.8 J (for 450 training set compounds) and 0.64≤R≤0.70 and 11.2 J≤RMSE≤12.4 J (for 170 test set compounds). The models were developed using Friedman Lack-of-Fit as a scoring function, which allows avoiding an overfitting. All the models have simple descriptors as basis functions and include linear splines. Furthermore, the applied descriptors do not require expensive calculation procedures, namely, non-empirical quantum-chemical calculations, complex iterative procedures, real space electron density analysis, etc. Most descriptors are based on structural and topological analysis and a part of them require very cheap semi-empirical PM6 calculations. The prediction takes a few minutes as an average, and most of the time is for the structure preparation and manual calculation of the descriptor "Increment", which is based on our recent incremental theory.
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http://dx.doi.org/10.1002/cphc.202400014 | DOI Listing |
J Med Internet Res
January 2025
Hospital Administration, Ramaiah Memorial Hospital, Bengaluru, Karnataka, India.
Background: Monitoring vital signs in hospitalized patients is crucial for evaluating their clinical condition. While early warning scores like the modified early warning score (MEWS) are typically calculated 3 to 4 times daily through spot checks, they might not promptly identify early deterioration. Leveraging technologies that provide continuous monitoring of vital signs, combined with an early warning system, has the potential to identify clinical deterioration sooner.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
School of Public Health, Capital Medical University, Beijing, China.
Background: Health inequalities among older adults become increasingly pronounced as aging progresses. In the digital era, some researchers argue that access to and use of digital technologies may contribute to or exacerbate these existing health inequalities. Conversely, other researchers believe that digital technologies can help mitigate these disparities.
View Article and Find Full Text PDFAJR Am J Roentgenol
January 2025
Department of Radiology, Division of Breast Imaging and Intervention, Mayo Clinic, Phoenix, AZ.
Contrast-enhanced mammography (CEM) is growing in clinical use due to its increased sensitivity and specificity compared to full-field digital mammography (FFDM) and/or digital breast tomosynthesis (DBT), particularly in patients with dense breasts. To perform an intraindividual comparison of MGD between FFDM, DBT, a combination protocol using both FFDM and DBT (combined FFDM-DBT), and CEM, in patients undergoing breast cancer screening. This retrospective study included 389 women (median age, 57.
View Article and Find Full Text PDFPLoS One
January 2025
Deptartment of Speech, Language, and Hearing Sciences, University of Colorado, Boulder, Colorado, United States of America.
Binaural speech intelligibility in rooms is a complex process that is affected by many factors including room acoustics, hearing loss, and hearing aid (HA) signal processing. Intelligibility is evaluated in this paper for a simulated room combined with a simulated hearing aid. The test conditions comprise three spatial configurations of the speech and noise sources, simulated anechoic and concert hall acoustics, three amounts of multitalker babble interference, the hearing status of the listeners, and three degrees of simulated HA processing provided to compensate for the noise and/or hearing loss.
View Article and Find Full Text PDFPLoS One
January 2025
Centro Ricerche Enrico Fermi, Rome, Italy.
The Covid-19 pandemic has sparked renewed attention to the risks of online misinformation, emphasizing its impact on individuals' quality of life through the spread of health-related myths and misconceptions. In this study, we analyze 6 years (2016-2021) of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major news sources-both questionable and reliable. We first use the symbolic transfer entropy analysis of news production time-series to dynamically determine which category of sources, questionable or reliable, causally drives the agenda on vaccines.
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